CSols Summit 2024 Recap: The Next-gen Lab: Pioneering Tomorrow, Today

Blog: CSols Summit 2024

It is becoming increasingly clear to organizations of all sizes, across all industries, that their most valuable product is their data. This is even true for service organizations like CSols. To help organizations make sense of their potential paths to better laboratory data management, CSols organized the 2024 edition of the CSols Summit. [Watch Recordings Here]

The Summit brought together industry experts to discuss how to move the lab forward with current tools and emerging technologies. Attendees gained better understandings of how artificial intelligence (AI), data, simulations, analytics, and lab informatics integrations could be optimized to improve their daily operations. Speakers who have leveraged these technologies inspired attendees with glimpses of a vision that may not yet be clear by sharing what the future of labs or organizations could be.

FAIR data became the unofficial theme of this year’s Summit. FAIR refers to data that is Findable, Accessible, Interoperable, and Reusable. Almost all of the speakers touched on the importance of data harmonization and FAIRification, which are key steps in reducing the prevalence of data silos. Many of the individual sessions were centered on the uses of data in laboratories, which of course brought FAIR into those discussions. Additional themes that arose from the sessions included data-driven decision-making, Industry 4.0 and the lab of the future, and digital transformation across your organization.

Data-driven Decision Making

The foundation of data-driven decision-making is FAIR data for compliance and flexibility. Middleware such as ZONTAL, which many organizations are beginning to adopt, relies on a standard dictionary or ontology based on FAIR data. This year, we were fortunate to have Ted Slater of EPAM, one of the authors of the FAIR Guiding Principles for Scientific Data Management and Stewardship, speak about Data Centricity and the FAIR Data Principles in Laboratory Informatics. But once you have FAIR data, what do you do with it?

The importance of a common ontology was reiterated by Simon Stier of the Fraunhofer Institute for Silicate Research in the AI in the Lab panel discussion, because it underpins the effective use of AI for mining data. Many organizations are turning to artificial intelligence (AI) to help them better use their troves of FAIR data. For example, users can benefit from the AI-powered features in the LabTwin voice-enabled digital laboratory assistant, which can help lab analysts develop new methods or be more efficient with their existing methods. Jeroen de Haas provided an overview of the ontology and large language model (LLM) behind LabTwin, in the session on LLMs and Mobile Tech: Achieving Real-time Data Capture and Providing Scientific Intelligence in the Lab.

The participants in the panels (AI in the Lab and Understanding What Lab Data Management Means to Your Lab) all had different takes on how AI will change or enhance their processes. Some of the potential issues in turning to AI that these speakers highlighted included the following:

  • Anca Ciobanu of the Pistoia Alliance presented the results of a survey with more than 200 respondents that highlighted the complexity of regulations around the use of AI, which vary by country.
  • Alex Wolicki of Genmab mentioned in the Lab Data Management panel that data pipelines are needed that can attest to the provenance of results and link them to the appropriate metadata in a relational database, as a way to drive AI and machine learning (ML) for better insights.
  • Glenn Everett of CSols spoke in the AI in the Lab panel about some of the ethical considerations around AI in research and development laboratories, such as data privacy and the inherent biases in AI models.
  • Susan Cleary of Novatek International, another panelist in the Lab Data Management session, added that the data must be able to be extracted from the system in a meaningful way to perform the statistical analyses possible with machine learning.

Industry 4.0 and the Lab of the Future

Industry 4.0 is changing laboratory workflows and enabling better decision-making through improved access to data. Greater integrations between systems and increased automation are hallmarks of Industry 4.0. Better insights from existing data, enabled by increased integrations, will drive Pharma 4.0 and other transformations across additional industries. One way that organizations are addressing the need for greater integration is with the adoption of middleware, such as scientific data platforms like Ganymede or ZONTAL.

In the session on Making the Platform Dream a Reality, David Hunt from ZONTAL provided background on how scientific data platforms are leveraging FAIR data to bring about Industry 4.0 and create the lab of the future. ZONTAL and other scientific data platforms integrate the flow of data across all of your lab instruments, to enable better decision making.

The session on Leveraging XR in the Lab Space was a view of the lab of the future that is already here. Miquel Vidal from CBRE provided examples of how using a set of lenses would enable laboratory technicians in one location to get troubleshooting or method help from support staff in another location. The uses of these extended reality (XR) lenses are probably limited only by our imaginations.

Digital Transformation Across Your Organization

One of the goals for the Summit sessions was to highlight Digital Transformation because it is key to how laboratories and their broader organizations are embracing innovation. To truly embrace digital transformation in all areas of your organization, you will need FAIR data, a laboratory data management strategy that accounts for how your data will flow from your various instruments and systems to the users who need it, and the cutting-edge tools to make data-driven decisions from that data.

It’s important to note that data privacy continues to be a top priority, as mentioned by Alexander Vang of Invivyd in the Laboratory Data Management panel. Vang spent a number of years at the U.S. Veteran’s Administration (VA) while they were implementing a new data management system. The sanctity of our veterans’ data privacy is paramount, so the VA’s system is a good model for preserving data privacy during a digital transformation.

Another important consideration of digital transformation that was highlighted in the summit is the people component—if the potential users of the system aren’t on board, there will be a lot of resistance to new software. Several examples of how to approach organizational change management (OCM) to achieve a return on investment (ROI) from the Lab of the Future were provided by Jennifer Heymont of Eisai in her session. It takes more than shiny new software to move an organization to a more digital way of life. Proper timing of the introductions, training, and behavior modifications are also important to achieve a digital transformation.

CSols Summit 2024 Recording Request

The CSols Summit 2024 provided several nuggets of valuable information to help labs move closer to Industry 4.0 through digital transformation and AI tools that can be leveraged now. We heard that the tighter integration between systems and instruments that are made possible by digital transformation enables greater efficiency and reduces human error. Formatting data correctly to flow from one environment to another is an important first step in making your testing workflows more efficient. Additionally, software improvements will continue to reduce data siloing, putting the right data in the hands of the appropriate decision-maker at exactly the right time. Thank you for joining us on the journey to the Lab of the Future.

If you’re ready to take the next step but are not sure where to begin, reach out to CSols and we’ll help you reach your lab data management goals.

What takeaways did you pull from the sessions offered at the CSols Summit 2024? Tell us about them in the comments.

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